Email this article Assessment of the Spectral Characteristics of Different Physiological Stages of Some Olive cvs and Its Relation with Productivity
Abstract
Remote sensing satellite imagery is the tool to obtain synoptic, multi-temporal, dynamic and timely efficient information about any target on Earth. The main objective of the current study is to use remote sensing satellite data and field spectral reflectance measurements to identify the spectral pattern of the different cultivars of olives and to statistically correlate this spectral reflectance pattern with crop productivity. The study was carried out in El-Beheira governorate (Wadi El Natrun) city during the whole year of 2014. The three observed varieties were Picual, Manzanillo and Kalamata. Measurements were carried out for five growth stages: dormancy stage, flowering stage, fruit sat stage, mature stage and ripening stage. The spectral reflectance pattern for each cultivar through the different growth stages was identified. Then, seven vegetation indices (normalized difference vegetation index (NDVI), modified chlorophyll absorption ration index (MCARI), triangular vegetation index (TVI), modified chlorophyll absorption ration index-1 (MCARI-1), modified chlorophyll absorption ration index-2 (MCARI-2), modified triangular vegetation index-2 (MTVI2) and chlorophyll index (CI)) were calculated through the five growth stages for each cultivar and then were observed as estimators for crop yield modeling. Analysis of the result based on the comparison of the correlation coefficient (r2) for all generated models, the target is to identify the optimal vegetation index and the optimal growth stage to predict yield for each variety. Generally, Manzanillo variety showed the highest reflectance followed by Picual and Kalamata. The result showed that the highest (r2) was with the two cultivars Picual and Kalamata during mature stage, while the highest (r2) was with cultivar Manzanillo during fruit sat stage. While the lowest (r2) was found during dormancy stage for the three cultivars.
Keywords
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.
*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)